Title:Prediction Of Arrival Of Nodes In A Scale Free Network

Abstract: Most of the networks observed in real life obey power-law degree
distribution. It is hypothesized that the emergence of such a degree
distribution is due to preferential attachment of the nodes. Barabasi-Albert
model is a generative procedure that uses preferential attachment based on
degree and one can use this model to generate networks with power-law degree
distribution. In this model, the network is assumed to grow one node every time
step. After the evolution of such a network, it is impossible for one to
predict the exact order of node arrivals. We present in this article, a novel
strategy to partially predict the order of node arrivals in such an evolved
network. We show that our proposed method outperforms other centrality measure
based approaches. We bin the nodes and predict the order of node arrivals
between the bins with an accuracy of above 80%.